neural network classifier training
Wear Particle Classifier System Based on an Artificial Neural Network
... The learning and classification procedures are described in Fig. 2. At the initial phase, pictures of four kinds of known wear particles are introduced into a system to extract their features, and they are then saved ...
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Robust Neural Network Classifier
... of neural networks, however, include their high tolerance of noisy dataas well as their ability to classify patterns on which they have not been ...and training a computer to pronounce English text. ...
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Perfecting Counterfeit Banknote Detection A Classification Strategy
... tree classifier, logistic regression classifier, artificial neural network classifier or multi layer perceptron, support vector machine and K Nearest Neighbours ...(KNN). ...
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Adaptive Neuro Fuzzy Inference System based Optical Character Recognition
... our classifier in case we are considering using data mining techniques for such ...ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input ...the ...
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Building and Improving Artificial Neural Network Classifier
... as training set data (80% in our experiment) and test set data (20% ...the training set as input and will give prediction on the test set which is completely unknown for the ...in training set, ...
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Neural Network Classifier for Isolated Character Recognition
... the training set from the input layer over the hidden layer(s) to the output layer, where each neuron sums the weighted inputs, passes them through the nonlinearity and passes this weighted sum to the neurons in ...
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An application of artificial neural network classifier for medical diagnosis
... a network training function that brings up-to-date weight and bias values as per the gradient descent with adaptive learning ...before training and in fact, the optimal learning rate varies during ...
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Detection of sodium oxalate needles in optical images using neural network classifiers
... If a General Regression Neural Network (GRNN) [4,5] is used as a classifier the number of training data points is taken to be in proportion with the a priori probability of occurre[r] ...
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A New Approach to Pollen Classification using Computational Intelligence
... used neural network for pollen identification and ...as classifier in that ...less training effort by introducing new selection criterion to obtain the most valuable training ...of ...
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Early Detection and Prediction of Lung Cancer Survival using Neural Network Classifier
... back-propagation neural network ensemble used as a classifier ...previously, neural network differs in various ways from traditional classifiers like Bayesian and k – nearest neighbor ...
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Self-Improving Generative Artificial Neural Network for Pseudo-Rehearsal Incremental Class Learning
... deep neural network system that acts both as generator and classifier of data to incrementally learn new classes of information present on data by generating samples from previous information, while ...
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BAT ALGORITHM FOR ROUGH SET ATTRIBUTE REDUCTION
... Grey neural network model combines the advantages of grey GM(1,1) model and neural network model, which suits for few sample data and volatile random ...grey neural network model ...
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Face Recognition using Rectangular Feature
... a neural network. This neural network consists of three layers, 10 neurons in the first layer, 20 neurons in the hidden layer and one neuron in the output ...the neural network ...
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Common Hybrid Feature Selection for Modeling Intrusion Detection System and Cyber Attack Detection System
... monitor network and system activities for malicious activities or policy violations and produces reports to a management ...ensuring network security and also this system is essential for protecting ...
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FACE DETECTION WITH SKIN COLOR AND FEATURES AND RECOGNIZATION USING GENETIC ALGORITHM
... to determine parameters of NN automatically and propose efficient GA which reduces its iterative computation time for enhancing the training capacity of NN. Proposed GA is based on steady model among continuous ...
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Handwritten Digit Recognition: Convolutional Neural Network as a Classifier
... The task of recognizing the handwriting of an individual from another is difficult as each personal possess a unique handwriting style. This is one reason why handwriting is considered as one of the main challenging ...
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Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning
... the 12 measurements of each eye of all five test subjects, were bundled into two groups: a group for central fixation (120 “eyes,” the “CF set”) and a group for paracentral fixation (480 “eyes,” the “para-CF set”). Data ...
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Assessment of Severity Level for Diabetic Macular Oedema Using Machine Learning Algorithms
... In view of the above stated reviews, in this proposed work, non-dilated retinal images are fed as input to the pre-processing stage. It corrects the problem of illumination variation that occurred during image ...
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Fuzzy approach for Arabic character recognition
... pixel neural network classifier value which character the performance of the classifiers will fixed dedicated for be different between different following points are taken into neural ne[r] ...
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Artificial Intelligence Based Power Quality Disturbance Analysis for Power Quality Improvement
... Artificial Neural Network (ANN) and Wavelet ...Artificial Neural Network for the classification of events. After training the neural network, the weight obtained is used ...
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